Nicolas Gerig (email@example.com)
Georg Rauter (firstname.lastname@example.org, BeurteilerIn)
|Inhalt||Nowadays, there is large knowledge available about control from a theoretical point of view. However, getting an entire setup working from hardware integration, safety, control, up to the graphical user interface or virtual environment, is seldom taught.
Participants will learn about basic differences in various automatization environments such as dSPACE, Matlab xPC Target, Matlab/Simulink, LabVIEW, and TwinCAT3. Within one week, the participants will learn how to integrate motors, sensors, and safety components in a predesigned electric cabinet for automation and control purposes. They will develop an automation application for a balancing and visual tracking application, integrate different control schemes, and write a graphical user interface to control the application in real-time.
In groups up to four, the participants will learn how to integrate different hardware components in a real-time control system (TwinCAT3, Beckhoff). They will learn how to account for software safety for an application involving servo motors. After successful hardware and software safety integration, different control schemes (model based controllers, non-linear controllers, vision-based non-linear controllers, etc. ) will be integrated in Matlab/Simulink. After compilation for TwinCAT3, the controllers will work on an industrial embedded real-time PC. During runtime, the participants will be able adapting controllers-online, record data, and see the influence of different filters. Consequently, the participants will program their own graphical user interface (GUI) in the game development engine UNITY. This GUI can be interfaced with the real-time environment through an Automation Device Specification (ADS), i.e. a field bus interface for TwinCAT3. After first experiments with the hard and software, two groups will work together for realizing a two-degrees of freedom ball balancing application, where each group controls one degree of freedom. The feedback loop will be closed through real-time vision-data that needs to be extracted applying feature extraction in real-time. Finally, the performance of the teams’ solutions to the challenging application is evaluated in a friendly competition.
|Lernziele||Hardware, and software integration in real-time applications.
Basic knowledge in applied control (model-based control, non-linear control, cascade control).
Real-time data extraction using computer vision algorithms.
GUI-programming for real-time applications.
|Bemerkungen||Please bring your own mask in case of team work.|
|Teilnahmebedingungen||Basic knowledge in control, automation, computer vision, Matlab/Simulink and Unity programming is of advantage, but not required.|
|Einsatz digitaler Medien||kein spezifischer Einsatz|
|Datum||20.09.2021 – 24.12.2021|
Keine Einzeltermine verfügbar, bitte informieren Sie sich direkt bei den Dozierenden.
Doktorat Biomedizinische Technik: Empfehlungen (Promotionsfach Biomedizinische Technik)
Modul: Image-Guided Therapy (Masterstudium: Biomedical Engineering)
|Hinweise zur Leistungsüberprüfung||Participants, who need credits for their lecture need to inform the lecturer at the begin of the lecture that they require ECTS credits. The according students will have to perform additional practical exercises before or after the lecture to verify that they understood the content of the course. The participants need to be present at least for 80% of the course and need to pass 4 out of 5 small practical exercises.
The course is rated as failed or passed.
|An-/Abmeldung zur Leistungsüberprüfung||An-/Abmelden: Belegen resp. Stornieren der Belegung via MOnA|
|Skala||Pass / Fail|
|Wiederholtes Belegen||beliebig wiederholbar|
|Zuständige Fakultät||Medizinische Fakultät|
|Anbietende Organisationseinheit||Departement Biomedical Engineering (DBE)|